scholarly journals Spatiotemporal Patterns of Ecosystem Restoration Activities and Their Effects on Changes in Terrestrial Gross Primary Production in Southwest China

2021 ◽  
Vol 13 (6) ◽  
pp. 1209
Author(s):  
Zhi Ding ◽  
Hui Zheng ◽  
Ying Liu ◽  
Sidong Zeng ◽  
Pujia Yu ◽  
...  
Keyword(s):  
Primary Production ◽  
Large Scale ◽  
Southwest China ◽  
Gross Primary Production ◽  
Ecosystem Restoration ◽  
Spatiotemporal Patterns ◽  
Observation Data ◽  
Entire Study ◽  
Field Observation Data ◽  
Moderate Resolution Imaging Spectroradiometer

Large-scale ecosystem restoration projects (ERPs) have been implemented since the beginning of the new millennium to restore vegetation and improve the ecosystem in Southwest China. However, quantifying the effects of specific restoration activities, such as afforestation and grass planting, on vegetation recovery is difficult due to their incommensurable spatiotemporal distribution. Long-term and successive ERP-driven land use/cover changes (LUCCs) were used to recognise the spatiotemporal patterns of major restoration activities, and a contribution index was defined to assess the effects of these activities on gross primary production (GPP) dynamics in Southwest China during the period of 2001–2015. The results were as follows. (1) Afforestation and grass planting were major restoration activities that accounted for more than 54% of all LUCCs in Southwest China. Approximately 96% of restoration activities involved afforestation, and these activities were mostly distributed around Yunnan Province. (2) The Breathing Earth System Simulator (BESS) GPP performed better than the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP validated by field observation data. Nevertheless, their annual GPP trends were similar and increased by 12,581 g C m−2 d−1 and 13,406 g C m−2 d−1 for MODIS and BESS GPPs, respectively. (3) Although the afforestation and grass planting areas accounted for less than 1% of the total area of Southwest China, they contributed to more than 1% of the annual GPP increase in the entire study area. Afforestation directly contributed 14.94% (BESS GPP) or 24.64% (MODIS GPP) to the annual GPP increase. Meanwhile, grass planting directly contributed only 0.41% (BESS GPP) or 0.03% (MODIS GPP) to the annual GPP increase.

scholarly journals A Comparison of OCO-2 SIF, MODIS GPP, and GOSIF Data from Gross Primary Production (GPP) Estimation and Seasonal Cycles in North America

2020 ◽  
Vol 12 (2) ◽  
pp. 258 ◽  
Author(s):  
Ruonan Qiu ◽  
Ge Han ◽  
Xin Ma ◽  
Hao Xu ◽  
Tianqi Shi ◽  
...  
Keyword(s):  
North America ◽  
Primary Production ◽  
Vegetation Type ◽  
Gross Primary Production ◽  
Remotely Sensed ◽  
Seasonal Cycles ◽  
Vegetation Types ◽  
Growth Trend ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Highly Correlated

Remotely sensed products are of great significance to estimating global gross primary production (GPP), which helps to provide insight into climate change and the carbon cycle. Nowadays, there are three types of emerging remotely sensed products that can be used to estimate GPP, namely, MODIS GPP (Moderate Resolution Imaging Spectroradiometer GPP, MYD17A2H), OCO-2 SIF, and GOSIF. In this study, we evaluated the performances of three products for estimating GPP and compared with GPP of eddy covariance(EC) from the perspectives of a single tower (23 flux towers) and vegetation types (evergreen needleleaf forests, deciduous broadleaf forests, open shrublands, grasslands, closed shrublands, mixed forests, permeland wetlands, and croplands) in North America. The results revealed that sun-induced chlorophyll fluorescence (SIF) data and MODIS GPP data were highly correlated with the GPP of flux towers (GPPEC). GOSIF and OCO-2 SIF products exhibit a higher accuracy in GPP estimation at the a single tower (GOSIF: R2 = 0.13–0.88, p < 0.001; OCO-2 SIF: R2 = 0.11–0.99, p < 0.001; MODIS GPP: R2 = 0.15–0.79, p < 0.001). MODIS GPP demonstrates a high correlation with GPPEC in terms of the vegetation type, but it underestimates the GPP by 1.157 to 3.884 gCm−2day−1 for eight vegetation types. The seasonal cycles of GOSIF and MODIS GPP are consistent with that of GPPEC for most vegetation types, in spite of an evident advanced seasonal cycle for grasslands and evergreen needleleaf forests. Moreover, the results show that the observation mode of OCO-2 has an evident impact on the accuracy of estimating GPP using OCO-2 SIF products. In general, compared with the other two datasets, the GOSIF dataset exhibits the best performance in estimating GPP, regardless of the extraction range. The long time period of MODIS GPP products can help in the monitoring of the growth trend of vegetation and the change trends of GPP.

scholarly journals Carbon dioxide dynamics in an agricultural headwater stream driven by hydrology and primary production

2020 ◽  
Vol 17 (9) ◽  
pp. 2487-2498 ◽  
Author(s):  
Marcus B. Wallin ◽  
Joachim Audet ◽  
Mike Peacock ◽  
Erik Sahlée ◽  
Mattias Winterdahl
Keyword(s):  
Carbon Dioxide ◽  
Primary Production ◽  
Large Scale ◽  
Co2 Concentration ◽  
Base Flow ◽  
Concentration Data ◽  
Entire Study ◽  
Co2 Concentrations ◽  
Variable Nature ◽  
Co2 Dynamics

Abstract. Headwater streams are known to be hotspots for carbon dioxide (CO2) emissions to the atmosphere and are hence important components in landscape carbon balances. However, surprisingly little is known about stream CO2 dynamics and emissions in agricultural settings, a land use type that globally covers ca. 40 % of the continental area. Here we present hourly measured in situ stream CO2 concentration data from a 11.3 km2 temperate agricultural headwater catchment covering more than 1 year (in total 339 d excluding periods of ice and snow cover). The stream CO2 concentrations during the entire study period were generally high (median 3.44 mg C L−1, corresponding to partial pressures (pCO2) of 4778 µatm) but were also highly variable (IQR = 3.26 mg C L−1). The CO2 concentration dynamics covered a variety of different timescales from seasonal to hourly, with an interplay of hydrological and biological controls. The hydrological control was strong (although with both positive and negative influences dependent on season), and CO2 concentrations changed rapidly in response to rainfall and snowmelt events. However, during growing-season base flow and receding flow conditions, aquatic primary production seemed to control the stream CO2 dynamics, resulting in elevated diel patterns. During the dry summer period, rapid rewetting following precipitation events generated high CO2 pulses exceeding the overall median level of stream CO2 (up to 3 times higher) observed during the whole study period. This finding highlights the importance of stream intermittency and its effect on stream CO2 dynamics. Given the observed high levels of CO2 and its temporally variable nature, agricultural streams clearly need more attention in order to understand and incorporate these considerable dynamics in large-scale extrapolations.

scholarly journals Spatiotemporal patterns of terrestrial gross primary production: A review

2015 ◽  
Vol 53 (3) ◽  
pp. 785-818 ◽  
Author(s):  
Alessandro Anav ◽  
Pierre Friedlingstein ◽  
Christian Beer ◽  
Philippe Ciais ◽  
Anna Harper ◽  
...  
Keyword(s):  
Primary Production ◽  
Gross Primary Production ◽  
Spatiotemporal Patterns

scholarly journals Identification of a general light use efficiency model for gross primary production

2011 ◽  
Vol 8 (4) ◽  
pp. 999-1021 ◽  
Author(s):  
J. E. Horn ◽  
K. Schulz
Keyword(s):  
Primary Production ◽  
Large Scale ◽  
Gross Primary Production ◽  
Daily Basis ◽  
Climatic Conditions ◽  
Environmental Drivers ◽  
Light Use Efficiency ◽  
Meteorological Forcing ◽  
Use Efficiency ◽  
Light Use Efficiency Model

Abstract. Non-stationary and non-linear dynamic time series analysis tools are applied to multi-annual eddy covariance and micrometeorological data from 44 FLUXNET sites to derive a light use efficiency model for gross primary production on a daily basis. The extracted typical behaviour of the canopies in response to meteorological forcing leads to a model formulation allowing for a variable influence of the environmental drivers temperature and moisture availability modulating the light use efficiency. Thereby, the model is applicable to a broad range of vegetation types and climatic conditions. The proposed model explains large proportions of the variation of the gross carbon uptake at the study sites while the optimized set of six parameters is well defined. With the parameters showing explainable and meaningful relations to site-specific environmental conditions, the model has the potential to serve as basis for general regionalization strategies for large scale carbon flux predictions.

scholarly journals Large-scale estimation and uncertainty analysis of gross primary production in Tibetan alpine grasslands

2014 ◽  
Vol 119 (3) ◽  
pp. 466-486 ◽  
Author(s):  
Honglin He ◽  
Min Liu ◽  
Xiangming Xiao ◽  
Xiaoli Ren ◽  
Li Zhang ◽  
...  
Keyword(s):  
Primary Production ◽  
Uncertainty Analysis ◽  
Large Scale ◽  
Gross Primary Production ◽  
Alpine Grasslands ◽  
Scale Estimation

scholarly journals Remote Sensing Evaluation of CLM4 GPP for the Period 2000–09*

2012 ◽  
Vol 25 (15) ◽  
pp. 5327-5342 ◽  
Author(s):  
Jiafu Mao ◽  
Peter E. Thornton ◽  
Xiaoying Shi ◽  
Maosheng Zhao ◽  
Wilfred M. Post
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Large Scale ◽  
Function Analysis ◽  
Gross Primary Production ◽  
Carbon Assimilation ◽  
Terrestrial Ecosystems ◽  
Northeast Russia ◽  
Community Land Model

Abstract Remote sensing can provide long-term and large-scale products helpful for ecosystem model evaluation. The authors compare monthly gross primary production (GPP) simulated by the Community Land Model, version 4 (CLM4) at a half-degree resolution with satellite estimates of GPP from the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product (MOD17) for the 10-yr period January 2000–December 2009. The assessment is presented in terms of long-term mean carbon assimilation, seasonal mean distributions, amplitude and phase of the annual cycle, and intraannual and interannual GPP variability and their responses to climate variables. For the long-term annual and seasonal means, major GPP patterns are clearly demonstrated by both products. Compared to the MODIS product, CLM4 overestimates the magnitude of GPP for tropical evergreen forests. CLM4 has a longer carbon uptake period than MODIS for most plant functional types (PFTs) with an earlier onset of GPP in spring and a later decline of GPP in autumn. Empirical orthogonal function analysis of the monthly GPP changes indicates that, on the intraannual scale, both CLM4 and MODIS display similar spatial representations and temporal patterns for most terrestrial ecosystems except in northeast Russia and in the very dry region of central Australia. For 2000–09, CLM4 simulated increases in annual averaged GPP over both hemispheres; however, estimates from MODIS suggest a reduction in the Southern Hemisphere (−0.2173 PgC yr−1), balancing the significant increase over the Northern Hemisphere (0.2157 PgC yr−1). The evaluations highlight strengths and weaknesses of the CLM4 primary production and illuminate potential improvements and developments.

scholarly journals Carbon dioxide dynamics in an agricultural headwater stream driven by hydrology and primary production

2020 ◽  
Author(s):  
Marcus B. Wallin ◽  
Joachim Audet ◽  
Mike Peacock ◽  
Erik Sahlée ◽  
Mattias Winterdahl
Keyword(s):  
Carbon Dioxide ◽  
Primary Production ◽  
Large Scale ◽  
Co2 Concentration ◽  
Headwater Stream ◽  
Concentration Data ◽  
Entire Study ◽  
Co2 Concentrations ◽  
Variable Nature ◽  
Co2 Dynamics

Abstract. Headwater streams are known to be hotspots for carbon dioxide (CO2) emissions to the atmosphere and are hence important components in landscape carbon balances. However, surprisingly little is known about stream CO2 dynamics and emissions in agricultural settings, a land-use type that globally cover ca 40 % of the continental area. Here we present continuously measured in-situ CO2 concentration data from a temperate agricultural headwater stream covering more than one year of open-water season. The stream CO2 concentrations during the entire study period were generally high (median 3.44 mg C L−1, corresponding to partial pressures (pCO2) of 4778 µatm) but were also highly variable (IQR = 3.26 mg C L−1). The CO2 concentration dynamics covered a variety of different time-scales from seasonal to hourly, and with an interplay of hydrological and biological controls. The hydrological control was strong (although with both positive as well as negative influences dependent on season) and CO2 concentrations changed rapidly in response to rainfall and snowmelt events. However, during growing-season baseflow and receding flow conditions, aquatic primary production seemed to control the stream CO2 dynamics resulting in elevated diel patterns. Given the observed high levels of CO2 and its temporally variable nature, agricultural streams clearly need more attention in order to understand and incorporate these considerable dynamics in large scale extrapolations.

scholarly journals Isoprene emissions track the seasonal cycle of canopy temperature, not primary production: evidence from remote sensing

2013 ◽  
Vol 10 (12) ◽  
pp. 19571-19601
Author(s):  
P. N. Foster ◽  
I. C. Prentice ◽  
C. Morfopoulos ◽  
M. Siddall ◽  
M. van Weele
Keyword(s):  
Primary Production ◽  
Air Temperature ◽  
Large Scale ◽  
Gross Primary Production ◽  
Canopy Temperature ◽  
Remotely Sensed ◽  
Formaldehyde Concentration ◽  
Tropical Regions ◽  
Isoprene Emission ◽  
Better Than

Abstract. Isoprene is important in atmospheric chemistry, but its seasonal emission pattern – especially in the tropics, where most isoprene is emitted – is incompletely understood. We set out to discover general, biome-independent relationships between large-scale isoprene emission and a series of potential predictor variables, including both observed and model-estimated variables related to gross primary production (GPP) and canopy temperature. To this end we used remotely sensed atmospheric concentrations of formaldehyde, an intermediate oxidation product of isoprene, as a proxy for isoprene emission in 22 regions selected to span high to low latitudes, to sample major biomes, and to minimize interference from pyrogenic sources of volatile organic compounds that could interfere with the isoprene signal. Formaldehyde concentrations showed the highest average seasonal correlations with remotely sensed (r = 0.85) and model-estimated (r = 0.80) canopy temperatures. Both variables predicted formaldehyde concentrations better than air temperature (r = 0.56) and a "reference" isoprene model that includes both temperature and GPP (r = 0.49), and far better than either remotely sensed green vegetation cover (r = 0.25) or model-estimated GPP (r = 0.14). GPP in tropical regions was anti-correlated with formaldehyde concentration (r = –0.30), which peaks during the dry season. We conjecture that the positive correlations of isoprene emission with primary production, and with air temperature, found in temperate forest regions arise simply because all three peak during the relatively short growing season. In most tropical regions, where the seasonal cycles of GPP and canopy temperature are very different, isoprene emission is revealed to depend on canopy temperature but not at all on GPP. The lack of a general correlation between GPP and formaldehyde concentration is consistent with experimental evidence that isoprene emission is decoupled from photosynthesis, and with the likely adaptive significance of isoprene emission in protecting leaves against heat damage and oxidative stress. In contrast, the high correlation between canopy temperature and formaldehyde concentration indicates the importance of including canopy temperature explicitly in large-scale models.

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